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    <title>LMArena - 大型语言模型竞技场</title>
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    <!-- Hero Section -->
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        <div class="max-w-6xl mx-auto text-center">
            <h1 class="text-5xl md:text-7xl font-bold mb-6 tracking-tight">
                LMArena
            </h1>
            <p class="text-2xl md:text-3xl mb-4 font-light">大型语言模型竞技场</p>
            <p class="text-lg md:text-xl max-w-3xl mx-auto opacity-90 leading-relaxed">
                通过匿名盲测方式比较不同大型语言模型的性能，让AI评估回归本质
            </p>
            <div class="mt-10 flex justify-center space-x-6">
                <a href="https://lmarena.ai/" target="_blank" class="bg-white text-purple-700 px-8 py-3 rounded-full font-semibold hover:bg-gray-100 transition duration-300 flex items-center">
                    <i class="fas fa-external-link-alt mr-2"></i>访问平台
                </a>
                <a href="#features" class="border-2 border-white px-8 py-3 rounded-full font-semibold hover:bg-white hover:text-purple-700 transition duration-300">
                    了解更多
                </a>
            </div>
        </div>
    </section>

    <!-- Problem Section -->
    <section class="py-16 px-6 bg-white">
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            <h2 class="text-4xl font-bold text-center mb-12 text-gradient">它能解决什么问题</h2>
            <div class="prose prose-lg max-w-none">
                <p class="text-gray-700 leading-relaxed mb-6">
                    <span class="drop-cap">在</span>评估大型语言模型时，用户常常面临主观偏差和基准测试的局限性：传统基准如GLUE或MMLU虽标准化，但往往脱离真实应用场景，导致模型在实验室表现优异却在生产环境中失效；手动比较多个模型响应耗时费力，且易受品牌偏好影响；此外，缺乏社区驱动的实时数据，用户难以追踪模型迭代后的实际改进，如从GPT-3.5到GPT-4的跃升。
                </p>
                <div class="bg-purple-50 p-6 rounded-xl border-l-4 border-purple-600">
                    <p class="text-gray-700 leading-relaxed">
                        LMArena采用盲测机制隐藏模型身份，确保投票基于纯响应质量；它聚合用户反馈形成公开排行榜，提供客观、众包式的性能指标；同时，支持保存历史和分享提示，降低重复劳动，让用户聚焦于洞见提取而非琐碎比较。
                    </p>
                </div>
            </div>
        </div>
    </section>

    <!-- Core Features -->
    <section id="features" class="py-16 px-6 bg-gray-50">
        <div class="max-w-6xl mx-auto">
            <h2 class="text-4xl font-bold text-center mb-12 text-gradient">核心功能概述</h2>
            <div class="grid md:grid-cols-2 gap-8">
                <div class="bg-white p-8 rounded-2xl card-hover">
                    <div class="text-5xl mb-4 feature-icon">
                        <i class="fas fa-eye-slash"></i>
                    </div>
                    <h3 class="text-2xl font-bold mb-3">盲测比较引擎</h3>
                    <p class="text-gray-600 leading-relaxed">
                        用户输入自定义提示，平台随机分配多个LLM生成响应，并隐藏模型名称供匿名投票。消除认知偏差，让开发者快速识别如Claude在创意写作中优于Gemini的细微优势。
                    </p>
                </div>
                <div class="bg-white p-8 rounded-2xl card-hover">
                    <div class="text-5xl mb-4 feature-icon">
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                    <h3 class="text-2xl font-bold mb-3">动态排行榜生成</h3>
                    <p class="text-gray-600 leading-relaxed">
                        基于全球用户投票实时更新模型Elo分数，覆盖写作、编码、多语言等类别。追踪模型演进，例如监控Llama 3的发布对Meta排名的影响。
                    </p>
                </div>
                <div class="bg-white p-8 rounded-2xl card-hover">
                    <div class="text-5xl mb-4 feature-icon">
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                    </div>
                    <h3 class="text-2xl font-bold mb-3">提示库与历史保存</h3>
                    <p class="text-gray-600 leading-relaxed">
                        内置社区分享的提示模板，允许账户用户保存聊天记录和投票历史。用户可复用先前提示避免从零开始，加速A/B测试流程。
                    </p>
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                <div class="bg-white p-8 rounded-2xl card-hover">
                    <div class="text-5xl mb-4 feature-icon">
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                    <h3 class="text-2xl font-bold mb-3">多模型集成支持</h3>
                    <p class="text-gray-600 leading-relaxed">
                        无缝接入主流LLM提供商，如OpenAI、Anthropic、Meta和Google模型。一站式访问，避免切换API密钥的繁琐。
                    </p>
                </div>
            </div>
        </div>
    </section>

    <!-- Use Cases -->
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            <h2 class="text-4xl font-bold text-center mb-12 text-gradient">使用场景</h2>
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                        <h3 class="text-2xl font-bold mb-3">模型选型在产品开发中</h3>
                        <p class="text-gray-600 leading-relaxed">
                            AI产品经理需为聊天机器人选择最佳LLM，通过输入典型用户查询，盲测投票后查看排行榜中Claude 3的胜率高于GPT-4o 15%，直接指导集成决策。缩短从评估到部署的周期约30%。
                        </p>
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                        <h3 class="text-2xl font-bold mb-3">研究论文的基准验证</h3>
                        <p class="text-gray-600 leading-relaxed">
                            学术研究者比较开源模型如Mistral与闭源如PaLM在多语言任务的表现。平台的历史投票数据显示Mistral在西班牙语翻译中Elo分数达1250，优于PaLM的1180。
                        </p>
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                        <h3 class="text-2xl font-bold mb-3">开发者社区反馈循环</h3>
                        <p class="text-gray-600 leading-relaxed">
                            独立开发者测试自定义微调模型对基准LLM的竞争力。通过分享提示到社区，收集数百票反馈，揭示模型在编码调试场景的弱点。
                        </p>
                    </div>
                </div>
            </div>
        </div>
    </section>

    <!-- Visualization -->
    <section class="py-16 px-6 bg-gray-50">
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            <h2 class="text-4xl font-bold text-center mb-12 text-gradient">平台架构概览</h2>
            <div class="mermaid">
                graph TD
                    A[用户输入提示] --> B{盲测引擎}
                    B --> C[GPT-4]
                    B --> D[Claude 3]
                    B --> E[Llama 3]
                    B --> F[Gemini]
                    C --> G[匿名响应A]
                    D --> H[匿名响应B]
                    E --> I[匿名响应C]
                    F --> J[匿名响应D]
                    G --> K{用户投票}
                    H --> K
                    I --> K
                    J --> K
                    K --> L[Elo分数更新]
                    L --> M[动态排行榜]
                    K --> N[历史记录]
                    N --> O[提示库]
                    style A fill:#667eea,stroke:#fff,color:#fff
                    style M fill:#764ba2,stroke:#fff,color:#fff
                    style K fill:#f59e0b,stroke:#fff,color:#fff
            </div>
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    </section>

    <!-- Getting Started Guide -->
    <section class="py-16 px-6 bg-white">
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            <h2 class="text-4xl font-bold text-center mb-12 text-gradient">上手指南</h2>
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                    <div class="step-number">1</div>
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                        <h3 class="text-xl font-bold mb-2">访问平台</h3>
                        <p class="text-gray-600">在浏览器中打开 https://lmarena.ai/，首页显示当前排行榜预览和"开始比